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Electrical & Computer Engineering
2012
Assessing the therapeutic effect of 630 nm lightemitting diodes irradiation on the recovery of
exercise-induced hand muscle fatigue with surface
electromyogram
Dandan Yang
Chongqing University
Xiaoying Wu
Chongqing University
Wensheng Hou
Chongqing University
Xiaolin Zheng
Chongqing University
Jun Zheng
New Mexico Institute of Mining and Technology
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Citation Information
Yang, D., Wu, X., Hou, W., Zheng, X., Zheng, J., Jiang, Y. (2012). Assessing the therapeutic effect of 630 nm light-emitting diodes
irradiation on the recovery of exercise-induced hand muscle fatigue with surface electromyogram. International Journal of Photoenergy,
2012 1-8.
http://digitalscholarship.unlv.edu/ece_fac_articles/656
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Authors
Dandan Yang, Xiaoying Wu, Wensheng Hou, Xiaolin Zheng, Jun Zheng, and Yingtao Jiang
This article is available at Digital Scholarship@UNLV: http://digitalscholarship.unlv.edu/ece_fac_articles/656
Hindawi Publishing Corporation
International Journal of Photoenergy
Volume 2012, Article ID 652040, 8 pages
doi:10.1155/2012/652040
Methodology Report
Assessing the Therapeutic Effect of 630 nm Light-Emitting Diodes
Irradiation on the Recovery of Exercise-Induced Hand Muscle
Fatigue with Surface Electromyogram
Dandan Yang,1 Xiaoying Wu,1 Wensheng Hou,1 Xiaolin Zheng,1
Jun Zheng,2 and Yingtao Jiang3
1 Key
Laboratory of Biorheological Science and Technology of Ministry of Education, Chongqing University, Chongqing 400044, China
of Computer Science and Engineering, New Mexico Institute of Mining and Technology, Socorro, NM 87801, USA
3 Department of Electrical and Computer Engineering, University of Nevada, Las Vegas, Las Vegas, NV 89154, USA
2 Department
Correspondence should be addressed to Wensheng Hou, [email protected]
Received 15 October 2011; Revised 24 January 2012; Accepted 27 February 2012
Academic Editor: Rui Duan
Copyright © 2012 Dandan Yang et al. This is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
This paper aims to investigate the effect of light emitting diode therapy (LEDT) on exercise-induced hand muscle fatigue by
measuring the surface electromyography (sEMG) of flexor digitorum superficialis. Ten healthy volunteers were randomly placed
in the equal sized LEDT group and control group. All subjects performed a sustained fatiguing isometric contraction with the
combination of four fingertips except thumb at 30% of maximal voluntary contraction (MVC) until exhaustion. The active LEDT
or an identical passive rest therapy was then applied to flexor digitorum superficialis. Each subject was required to perform a
re-fatigue task immediately after therapy which was the same as the pre-fatigue task. Average rectified value (ARV) and fractal
dimension (FD) of sEMG were calculated. ARV and FD were significantly different between active LEDT and passive rest groups
at 20%–50%, 70%–80%, and 100% of normalized contraction time (P < 0.05). Compared to passive rest, active LEDT induced
significantly smaller increase in ARV values and decrease in FD values, which shows that LEDT is effective on the recovery of
muscle fatigue. Our preliminary results also suggest that ARV and FD are potential replacements of biochemical markers to assess
the effects of LEDT on muscle fatigue.
1. Introduction
Muscular fatigue is manifested by a decline in the forcegenerating capacity during maximal contraction [1] or the
incapacity to maintain required or expected muscle force for
a period of time [2]. This phenomenon is dependent on
the type and intensity of exercise, muscle group involved,
duration of the activity, and type and size of muscle
fibers [3], which means that fatigue development is a
complex and multifaceted process involving physiological,
biomechanical and psychological elements [2]. Although
the mechanisms of fatigue development, prevention and
recovery are not fully understood up to now, fatigue recovery
is of great interest to the researchers due to its great significance in rehabilitation medicine, ergonomics and sports
science.
In the last decades, a large amount of physical or
chemical approaches have been attempted. The positive
therapeutic effects of cryotherapy [4, 5], neuromuscular
electrical stimulation [6], and antioxidant supplementation
[7] were testified for muscle fatigue recovery. Recently,
phototherapy or optical irradiation with specific wavelength
range was proposed for fatigue development and recovery
of skeletal muscle [8–10]. Studies on animal and human
revealed that the low level laser therapy (LLLT) significantly
promotes the muscle fatigue development and recovery by
reducing postexercise blood lactate, decreasing the activity
of creatine kinase (CK) and C-reactive protein (CRP),
and improving muscle performance [8, 11–14]. Further
experiments indicated that the phototherapeutic effects of
LLLT were associated with depressing of oxidative stress [15]
and reactive oxygen species production [16], improvement of
2
mitochondrial function [17, 18] and ATP synthesis [19], and
enhancement of microcirculation [20]. As an alternative light
source, high-power light-emitting diodes (LEDs) exhibited
similar phototherapeutic effects, and the decreased muscle
fatigue was observed through phototherapy with lightemitting diodes therapy (LEDT) [9, 10, 21, 22]. However,
the mechanism for phototherapy in human muscle fatigue
recovery is far from clear.
Muscle fatigue is a progressive course of decreasing
muscle activity accompanied by physical and chemical
change in muscles. Knowledge of these dynamic changes is
helpful to evaluate the effects of phototherapy and make
better treatment plan. Although the physiochemical changes
in CK activity and CRP levels are commonly used as indirect
indicators for the estimation of muscle fatigue [10, 23], it is
not suitable for continuously monitoring the physiological
state of muscle during muscle contraction. Surface electromyography (sEMG) is a widely used electrophysiological
technique for muscle activity detection and has been used
to characterize the myoelectric properties of muscle fatigue
in onset time of mechanical fatigue [24, 25] as well as
the neuromuscular properties of muscle fatigue [26]. The
average rectified value (ARV) and fractal dimension (FD)
of sEMG were successfully used to evaluate the status of
muscle fatigue by measuring firing rate of motor unit and
its recruitment pattern [2, 27, 28]. The present study is to
investigate the effect of phototherapy on hand muscle fatigue
recovery by comparing the ARV and FD values of the LEDT
and control groups.
2. Materials and Methods
2.1. Subjects. Ten healthy right-handed university students
(five males and five females) volunteered to participate in
the experiment. The participants’ inclusion criteria were (i)
healthy with no history of myopathology and neuropathology and (ii) free of intense exercise in 24 hours before
the experiment. Experiments were conducted after receiving
approval from the local ethics committee. Each participant
was given an oral and written summary of the experimental
protocol and the purpose of the study and then was required
to sign a consent form prior to the experiment.
2.2. Experimental Procedure. Each subject was seated upright
in a comfortable chair with his/her hip and knee joint
flexed at 90◦ and their right forearm resting on a supporting
armrest. The elbow was positioned in palm downward with
the elbow in approximately 120◦ flexion. The forearm and
wrist were stabilized to the armrest with nylon tapes. The
subject was asked to produce a force with the combination
of four fingers (IMRL, I = index, M = middle, R = ring,
and L = little). The exerted forces of individual fingers were
recorded by four load cells (linear operation range 0–196 N,
JLBS, JinNuo Inc., China), respectively.
The experimental protocol consisted of the following
phases.
Phase 1 (Maximal Voluntary Contraction (MVC) of IMRL).
MVC was taken as the maximum of three isometric
International Journal of Photoenergy
contractions of the right forearm flexors with IMRL. Each
contraction lasted 5 seconds with 2-minute recovery period
between two successive contractions. Verbal encouragement
was provided during MVCs to obtain maximal effort. A 5minute rest was taken after the maximal contractions.
Phase 2 (Prefatigue). Immediately after Phase 1, the subject
was instructed to perform a sustained fatiguing isometric
contraction at 30% MVC of IMRL until exhaustion, which
was defined as the point at which the force decreased by 5%
of the target force for more than 2 seconds [29].
Phase 3 (Therapy). Participants were randomly divided into
two equal sized groups, that is, active LEDT group (four
males and one female) and passive rest therapy group (one
male and four females). The fatigued forearm hand muscle
received an active LEDT through photon therapy equipment
with a multidiode cluster probe (Carnation66, Shenzhen
Lifotronic Tech. Inc., Shenzhen, China) or a passive rest
therapy. The therapy started immediately after Phase 2 and
ended 120 seconds before Phase 4.
The subject’s forearm was positioned in neutral rotation
during the therapy. For LEDT, the center of the light spot
was located at approximately 50% of landmark line from the
medial epicondyle to the styloid process of the ulna, which
was the center belly of flexor digitorum superficialis. The
subject’s forearm maintained the rest state without moving
during the therapy.
Irradiation with LEDT (100 LEDs with wavelength
630 nm, spot size 2.5 cm2 , power density 0.048 W/cm2 , and
energy density 57.6 J/cm2 ) was performed in non-contact
mode with the probe held stationary at a vertical distance of
60 mm between light source and skin surface. The total time
of therapy was 20 minutes. Opaque goggles were used for all
participants during the therapy to protect their eyes from the
treatment and assure the blindness of the study.
Phase 4 (Refatigue). Each subject was required to perform
the same fatigue task again as that did in Phase 2.
The actual force of IMRL (30% MVC) was recorded
by a 12-bit data acquisition card (USB-6008, National
Instruments, USA) in Phases 1, 2, and 4. The actual force
and the target force were shown on an LCD screen for
real-time visual feedback to the subject in Phases 2 and 4.
Surface EMG signals were recorded from the right flexor
digitorum superficialis during Phases 1, 2, and 4. The skin
was prepared by abrading and cleaning the recording area
with alcohol. sEMG signals were detected by two disposable
ECG electrodes (Shanghai LITU Medical Appliances Co.,
model LT-601, China) with 20 mm interelectrode distance.
The electrodes were centered around the 50% point on the
line joining the medial epicondyle to the styloid process of
the ulna [29]. A groud electrode was attached on the dorsal
surface of the wrist. All force signals and sEMG signals were
recorded simultaneously by the multichannel physiological
recorder apparatus (RM6280, Chengdu Instrumentation
Inc.) at a rate of 2000 samples per second. The force and
sEMG signals were band-pass filtered within the frequency
ranges of 0–30 Hz and 8–500 Hz, respectively. The quality
International Journal of Photoenergy
3
of the sEMG signal was visually checked before actual
recording.
2.3. Data Analysis. Data analysis was performed off line
using MATLAB 7.8 (The MathWorks Inc., Natick, USA).
Bipolar signals were band-pass filtered using a seven-order
elliptic filter (10 Hz–500 Hz). Bipolar sEMG signals of flexor
digitorum superficialis during fatigue were divided into
several 10 s segments. For each 10 s segment, a 2048-point
sEMG epoch was equally divided into four subepochs to
estimate ARV and FD. The ARV and FD values obtained
from the 4 subepochs were averaged to get the overall
estimations of ARV and FD for each 10 s segment.
The ARV of sEMG signal in the time domain is defined
as
ARV =
N
1 |x i |,
N i=1
(1)
where xi is the ith sample of the signal and N is the number
of the samples in the epoch.
The FD value of a one-dimensional physiological signal
is calculated as the change in length of recursively defined
self-similar curves with the measurement scale. The length of
curve used in computation was the same as described in the
study of Arjunan and Kumar [30]. By plotting the logarithm
of the average length of curve versus the logarithm of the time
interval, the FD is obtained as the slope of the fitted linear
line.
The ARV values were normalized by values obtained at
100% MVC contraction because the absolute level of each
parameter differed among the subjects. Because the task
time was not the same for each subject, we normalized the
contraction time by setting the task time as 100%. The task
time of each subject was divided into 10 equal segments. The
variables were obtained at every 10% time. If there was no
measurement value at the resampled point, an interpolated
value was calculated from the nearest sampled values. The
ARV and FD values were averaged for all the subjects in the
same group.
2.4. Statistical Analysis. Statistical analysis was performed
with SPSS 13.0 in our study. Normality of the distributions
of ARV and FD values was checked with the KolmogorovSmirnov test prior to statistical testing, and the results were
positive. The t-test was employed to test if there was a
significant difference between the two experimental groups
for ARV and FD of prefatigue task at the end of the fatiguing
contraction (i.e., 100% of contraction time). The difference
between two types of therapies during refatigue task was also
assessed by the t-test at each time instant. Changes in ARV
and FD over the normalized contraction time were assessed
by linear regression analysis. Results were reported as mean
and standard deviation (SD) in the text and standard error
(SE) in the figures. The level of statistical significance was set
at 0.05.
Table 1: Results of linear regression analysis for refatigue task.
sEMG parameter
ARV
FD
Group
LEDT
Control
LEDT
Control
Slope
0.136
0.227
−0.015
−0.04
P value
<0.001
0.001
0.085
0.002
3. Results
The average age of participants was 23.7 years (SD ± 1.19).
Their average weight and height were 55.6 kg (SD ± 9.38)
and 165.5 cm (SD ± 6.70), respectively. For the LEDT group,
the average lengths of the prefatigue and refatigue tasks were
815.4 s (SD ± 595.3) and 311.4 s (SD ± 109.5), respectively,
and the average lengths of the prefatigue and refatigue tasks
for passive rest therapy group were 1000.0 s (SD ± 254.7) and
602.8 s (SD ± 195.8), respectively.
Examples of the sEMG signals recorded from the right
flexor digitorum superficialis and the forces generated during
the experiment from the two treatment groups are presented
in Figure 1. Figures 2 and 3 plot the changes of mean normalized ARV and mean FD along the normalized contraction
time, respectively. From Figures 2(a) and 2(c), it can be
observed that there are no statistical differences for ARV
between the LEDT and control groups at the 100% of the
normalized contraction time during prefatigue task (P >
0.05). The same observation can be obtained from Figures
3(a) and 3(c) for FD.
The results of linear regression analysis for the refatigue
task are summarized in Table 1. The mean ARV value of
the two groups increases over contraction time after both
therapies as shown in Table 1. Comparing Figure 2(b) with
Figure 2(d), the mean ARV values of the LEDT group are
significantly smaller than those of the passive rest therapy
group. The statistically significant difference between the
mean ARV values of the two groups can be found at 10%–
50%, 70%–80%, and 100% of the normalized contraction
time (P < 0.05).
From Figure 3(d), it can be easily observed that the
mean FD value decreases over the normalized contraction
time for passive rest group, which is confirmed by Table 1
(P < 0.05). For active LEDT group, the mean FD value
has a trend of decreasing (Figure 3(b)) but not statistically
significant (P = 0.085 as shown in Table 1). The statistically
significant difference between the mean FD values of the two
groups can be found at 0% and 20%–100% of the normalized
contraction time (P < 0.05).
4. Discussion
Unlike biochemical indicators commonly used in previous
studies, we attempt to use electrophysiological indicators for
assessing the therapeutic effect of LEDT on the recovery of
muscle fatigue. The aim of the present study is to verify
the effect of LEDT by using two parameters extracted from
sEMG: ARV and FD. There was no significant difference
between the two groups for both sEMG variables during
4
International Journal of Photoenergy
500
Prefatigue
sEMG (V)
sEMG (V)
500
0
−500
Time (s)
0
−500
6
6
4
4
2
2
0
Time (s)
Time (s)
(c)
(d)
500
sEMG (V)
500
sEMG (V)
Time (s)
(b)
Force (N)
Force (N)
(a)
0
Refatigue
0
0
−500
−500
Time (s)
Time (s)
(f)
15
15
10
10
Force (N)
Force (N)
(e)
5
0
Time (s)
(g)
5
0
Time (s)
(h)
Figure 1: Examples of sEMG signals and the forces recorded during the prefatigue and refatigue tasks until exhaustion for the two types of
treatments. (a)–(d) Passive rest therapy, (e)–(h) Active LEDT.
prefatigue task. Therefore, any difference found in ARV
and FD between the two groups during the refatigue task
cannot be attributed to possible pre-existing uncontrolled
differences. Compared with passive rest, irradiation of the
flexor digitorum superficialis with active LEDT after muscle
fatigue resulted in significantly smaller changes of ARV and
FD values along the contraction time, which demonstrates
that LEDT is effective at accelerating fatigue recovery.
Compared with passive rest group, smaller increase of the
sEMG ARV value along the contraction time was observed
5
0.5
Normalized ARV
Normalized ARV
International Journal of Photoenergy
0.4
0.3
0.5
0.4
0.3
0.2
0.2
0
20
40
60
Contraction time (%)
80
0
100
20
40
60
Contraction time (%)
(a)
100
∗
∗
∗
40
60
Contraction time (%)
80
100
(b)
0.8
0.8
0.7
Normalized ARV
Normalized ARV
80
0.6
0.5
0.4
0.3
0.2
0
20
40
60
Contraction time (%)
80
100
(c)
0.7
0.6
∗
0.5
∗
∗
∗
∗
0.4
0.3
0.2
0
20
(d)
Figure 2: The changes of the mean normalized ARV (±SE) over the normalized contraction time during the prefatigue (a) and refatigue (b)
tasks of the LEDT group and the prefatigue (c) and refatigue (d) tasks of the passive rest group. The symbol “∗” indicates that the difference
between the mean normalized ARV values of the two groups in the corresponding normalized contraction time is statistically significant.
in LEDT group, indicating that prefatigued muscle tends to
return to its initial unfatigue state faster after LEDT. The
sEMG ARV reflects the total potential of activating motor
units during muscle contraction. Thus, increased ARV value
under sustained contraction is mainly a consequence of the
recruitment of additional motor units and the decrease of
conduction velocity [31]. Hence, if muscle function did not
recover completely from prefatigue status, it might need
to recruit more new motor units to maintain the desired
force, which results in larger and faster increase of sEMG
ARV. Previous studies have reported that sEMG amplitude
can return to their initial status from fatigue status after
appropriate rest [32, 33]. However, 20 min recovery is not
enough to enable the flexor digitorum superficialis to fully
recover after long fatiguing contraction at low force level
[34]. Therefore, the difference of the ARV values between
the two groups in our study suggests that LEDT certainly has
effect on fatigue recovery and refatigue development.
It can also be observed that although the ARV value
increases along the contraction time, it never exceeds 40%–
60% of the maximal level for the active LEDT group and
70%–90% of the maximal level for the passive rest group
(Figure 2). Moreover, the ARV value at 100% of contraction
time is group dependent (P < 0.05). Previous studies
found that the central fatigue occurred inevitably at lower
contraction intensities when muscle fatigue was induced
by means of sustained, submaximal isometric contractions
of limb and hand muscles [35]. Recent studies also found
deficit in average EMG in hand and limb muscles of subjects
performing submaximal contractions, which was inversely
related to contraction intensity, with much larger deficits in
the low-intensity task [36]. In our study, we observed that
the LEDT group has smaller sEMG deficits than the control
group, suggesting the inhibiting effect of LEDT on driving
motor neurons.
Next, significant decrease of FD value over contraction
time was observed in the passive rest group in our study. The
FD of sEMG signal can be used to quantify the complexity
of motor unit recruitment patterns [25, 27]. The decrease
in discharge rate of motor unit as well as the increase in
duration of motor unit action potentials [37] and the level
of synchronism of motor units [38] would account for the
decrease of FD during the refatigue task as an adaptation
to muscle fatigue. When the task muscle without recovering
completely from fatigue status was required to work again,
the larger changes of the three aforementioned factors would
lead to distinct reduction of FD over contraction time. Many
studies reported that the power spectrum of sEMG showed
an increased median frequency during recovery [39], which
is the opposite process with the development of muscle
fatigue. This means that the change of FD for the LEDT
group should be smaller than that of the passive rest group,
which is in accordance with our results. Moreover, the differences between the two treatment groups occur at all instants
except for 10% contraction time, which is another indication
of the LEDT effect on skeletal muscle fatigue recovery.
International Journal of Photoenergy
2
2
1.95
1.95
FD
FD
6
1.9
1.85
1.85
1.8
1.9
0
20
40
60
Contraction time (%)
80
1.8
100
0
20
40
60
Contraction time (%)
2
2
1.95
1.95
1.9
1.9
∗
1.85
∗
1.85
∗
1.8
0
20
40
100
(b)
FD
FD
(a)
80
60
80
100
Contraction time (%)
(c)
1.8
0
20
∗
∗
∗
∗
∗
40
60
Contraction time (%)
∗
80
∗
100
(d)
Figure 3: The changes of mean FD (±SE) over the normalized contraction time during the prefatigue (a) and refatigue (b) tasks of the LEDT
group and the prefatigue (c) and refatigue (d) tasks of the passive rest group. The symbol “∗” indicates that the difference between the mean
FD values of the two groups in the corresponding normalized contraction time is statistically significant.
In addition, it can be observed that the two sEMG
parameters show difference in the time instances to be
statistically significant different between the two groups. One
possible explanation is that the two parameters characterize
muscle fatigue from a couple of different perspectives.
Compared with FD, ARV is an indicator of sEMG amplitude
in the time domain, which is more sensitive to peripheral
fatigue [28]. FD is the most promising index of central
fatigue and mainly affected by the level of motor unit
synchronization and weakly affected by either conduction
velocity or fat layer thickness [25, 28]. However, the lack
of consistency of EMG amplitude is a common problem to
many electromyographic studies. Therefore, it was suggested
that myoelectric manifestations of fatigue could be better
described by two parameters sensitive to central and peripheral fatigue, respectively [28].
A recent study found that LEDT administrated before
exercise could cause a slight delay in the development of
skeletal muscle fatigue, decrease blood lactate levels, and
inhibit the release of CK and CRP [9]. An other study
has also found that LEDT has potential to improve shortterm postexercise recovery by inhibiting postexercise increase
in blood lactate level and CK activity [10]. Although the
exact working mechanism of LEDT remains unknown,
some thought that this positive effect might be due to the
improved peripheral microcirculation [20], increment in
mitochondrial capacity [17, 18], reduced oxidative stress
[15], and decreased reactive oxygen species [16] after laser
treatment. These changes could improve the muscle fatigue
recovery at cellular level [34] and thus affect the physiologic
properties of fatigued muscles. If complete blood perfusion
is allowed, the metabolic products occurred during muscle
fatigue are rapidly washed out so that the muscle fiber
membrane function [40] and sEMG spectral content [41]
almost completely recover after 5 min rest. Thus, these
positive results of LEDT or LLLT indirectly support our
findings, suggesting that ARV and FD can be served as valid
parameters to investigate the changes of muscle function
induced by LLLT or LEDT.
There are several limitations in our study. We investigated
the effects of a commercially available LEDT device with
red wavelength on muscle fatigue recovery by means of
noninvasive sEMG analysis and the preliminary results
were encouraging. However, the exact mechanism of how
LEDT promotes short-term fatigue recovery cannot be
revealed in our study. We also observed that the trend
of ARV and FD for one subject was opposite to others,
which might be due to the architectural and functional
complexity of flexor digitorum superficialis [42] and spatialdependence center strategy to cope with fatigue [43].
Due to the small sample size, the preliminary results
obtained in our study need to be examined with a largescale study by using both biochemical marks and sEMG
parameters.
International Journal of Photoenergy
7
5. Conclusion
The aim of this study is to use the electrophysiological
parameters extracted from sEMG signals instead of biochemical indicators for assessing the effect of light therapies on
muscle fatigue recovery. Two electrophysiological parameters
used in our experiments, ARV and FD, are sensitive to
peripheral and central fatigue, respectively. Our experimental results showed that active LEDT induced smaller
increasing rate of ARV and decreasing rate of FD than passive
rest over sustained contraction time during the refatigue
task, which suggests that active LEDT recovers the muscle
fatigue faster than passive rest. In the future, we will conduct
a larger-scale study involving more subjects and search for
other useful electrophysiological parameters.
[9]
[10]
[11]
Acknowledgments
This work was supported by the National Natural Foundation of China (no. 30770546, 30970758), the Fundamental
Research Funds for the Central Universities (no. 10231116),
Chongqing Natural Sciences Foundation (no. 2010AB5087,
2010AA5049) and the Third Stage of “211 Project” of
Innovation Talent Training Project in Chongqing University
(no. S-09104). The authors would like to thank all the
volunteers in this study.
[12]
[13]
[14]
References
[1] E. Verburg, H. M. S. Thorud, M. Eriksen, N. K. Vøllestad,
and O. M. Sejersted, “Muscle contractile properties during
intermittent nontetanic stimulation in rat skeletal muscle,”
American Journal of Physiology-Regulatory Integrative and
Comparative Physiology, vol. 281, no. 6, pp. R1952–R1965,
2001.
[2] M. Cifrek, V. Medved, S. Tonković, and S. Ostojić, “Surface
EMG based muscle fatigue evaluation in biomechanics,”
Clinical Biomechanics, vol. 24, no. 4, pp. 327–340, 2009.
[3] F. Billaut, F. A. Basset, M. Giacomoni, F. Lemaı̂tre, V. Tricot,
and G. Falgairette, “Effect of high-intensity intermittent
cycling sprints on neuromuscular activity,” International Journal of Sports Medicine, vol. 27, no. 1, pp. 25–30, 2006.
[4] M. Utsunomiya, K. Nitta, H. Sawagichi et al., “Changes in
blood flow, temperature and muscle endurance in association
with Cryotherapy,” Journal of Physical Therapy Science, vol. 22,
no. 1, pp. 43–49, 2010.
[5] V. Perciavalle, M. Coco, and L. Gurrisi, “Alternating hot and
cold water immersion accelerates blood lactate decrease after
maximal anaerobic exercise,” Acta Medica Mediterranea, vol.
24, no. 1, pp. 19–21, 2008.
[6] N. Babault, C. Cometti, and N. A. Maffiuletti, “Does electrical
stimulation enhance post-exercise performance recovery?”
European Journal of Applied Physiology, vol. 111, no. 1, pp.
2501–2507, 2011.
[7] A. Dutta, K. Ray, V. K. Singh, P. Vats, S. N. Singh, and S. B.
Singh, “L-carnitine supplementation attenuates intermittent
hypoxia-induced oxidative stress and delays muscle fatigue in
rats,” Experimental Physiology, vol. 93, no. 10, pp. 1139–1146,
2008.
[8] E. C. Pinto Leal Jr., R. A. Brandao Lopes-Martins, L.
Frigo et al., “Effects of low-level laser therapy (LLLT) in
[15]
[16]
[17]
[18]
[19]
[20]
[21]
the development of exercise-induced skeletal muscle fatigue
and changes in biochemical markers related to postexercise
recovery,” Journal of Orthopaedic & Sports Physical Therapy,
vol. 40, no. 8, pp. 524–532, 2010.
E. C. P. Leal Junior, A. B. Lopes-Martins, B. M. Baroni et
al., “Comparison between single-diode low-level laser therapy
(LLLT) and LED multi-diode (cluster) therapy (LEDT) applications before high-intensity exercise,” Photomedicine and
Laser Surgery, vol. 27, no. 4, pp. 617–623, 2009.
E. C. Leal Junior, V. de Godoi, J. L. Mancalossi et al., “Comparison between cold water immersion therapy (CWIT) and light
emitting diode therapy (LEDT) in short-term skeletal muscle
recovery after high-intensity exercise in athletes-preliminary
results,” Lasers in Medical Science, vol. 26, no. 4, pp. 493–501,
2011.
R. Á. B. Lopes-Martins, R. L. Marcos, P. S. Leonardo et al.,
“Effect of low-level laser (Ga-Al-As 655 nm) on skeletal muscle
fatigue induced by electrical stimulation in rats,” Journal of
Applied Physiology, vol. 101, no. 1, pp. 283–288, 2006.
B. M. Baroni, E. C. P. Leal Junior, T. De Marchi, A. L.
Lopes, M. Salvador, and M. A. Vaz, “Low level laser therapy
before eccentric exercise reduces muscle damage markers in
humans,” European Journal of Applied Physiology, vol. 110, no.
4, pp. 789–796, 2010.
E. C. P. Leal Junior, R. Á. B. Lopes-Martins, B. M. Baroni et al.,
“Effect of 830 nm low-level laser therapy applied before highintensity exercises on skeletal muscle recovery in athletes,”
Lasers in Medical Science, vol. 24, no. 6, pp. 857–863, 2009.
E. C. P. Leal Junior, R. Á. B. Lopes-Martins, P. De Almeida,
L. Ramos, V. V. Iversen, and J. M. Bjordal, “Effect of lowlevel laser therapy (GaAs 904 nm) in skeletal muscle fatigue
and biochemical markers of muscle damage in rats,” European
Journal of Applied Physiology, vol. 108, no. 6, pp. 1083–1088,
2010.
L. I. Fillipin, J. L. Mauriz, K. Vedovelli et al., “Low-level laser
therapy (LLLT) prevents oxidative stress and reduces fibrosis
in rat traumatized Achilles tendon,” Lasers in Surgery and
Medicine, vol. 37, no. 4, pp. 293–300, 2005.
Y. Fujimaki, T. Shimoyama, Q. Liu, T. Umeda, S. Nakaji, and K.
Sugawara, “Low-level laser irradiation attenuates production
of reactive oxygen species by human neutrophils,” Journal of
Clinical Laser Medicine and Surgery, vol. 21, no. 3, pp. 165–
170, 2003.
T. Karu, “Mitochondrial mechanisms of photobiomodulation
in context of new data about multiple roles of ATP,” Photomedicine and Laser Surgery, vol. 28, no. 2, pp. 159–160, 2010.
P. C. L. Silveira, E. L. Streck, and R. A. Pinho, “Evaluation
of mitochondrial respiratory chain activity in wound healing
by low-level laser therapy,” Journal of Photochemistry and
Photobiology B, vol. 86, no. 3, pp. 279–282, 2007.
P. C. L. Silveira, L. A. D. Silva, D. B. Fraga, T. P. Freitas, E. L.
Streck, and R. Pinho, “Evaluation of mitochondrial respiratory
chain activity in muscle healing by low-level laser therapy,”
Journal of Photochemistry and Photobiology B, vol. 95, no. 2,
pp. 89–92, 2009.
F. R. M. Ihsan, “Low-level laser therapy accelerates collateral
circulation and enhances microcirculation,” Photomedicine
and Laser Surgery, vol. 23, no. 3, pp. 289–294, 2005.
E. C. P. Leal, R. Á. B. Lopes-Martins, R. P. Rossi et al., “Effect
of cluster multi-diode Light Emitting Diode Therapy (LEDT)
on exercise-induced skeletal muscle fatigue and skeletal muscle
recovery in humans,” Lasers in Surgery and Medicine, vol. 41,
no. 8, pp. 572–577, 2009.
8
[22] B. M. Baroni, E. C. P. Leal, J. M. Geremia, F. Diefenthaeler, and
M. A. Vaz, “Effect of light-emitting diodes therapy (LEDT)
on knee extensor muscle fatigue,” Photomedicine and Laser
Surgery, vol. 28, no. 5, pp. 653–658, 2010.
[23] Y. Zhou, Y. Li, and R. Wang, “Evaluation of exercise-induced
muscle damage by surface electromyography,” Journal of
Electromyography and Kinesiology, vol. 21, no. 2, pp. 356–362,
2011.
[24] S. Kumar and Y. Narayan, “Spectral parameters of trunk
muscles during fatiguing isometric axial rotation in neutral
posture,” Journal of Electromyography and Kinesiology, vol. 8,
no. 4, pp. 257–267, 1998.
[25] A. Troiano, F. Naddeo, E. Sosso, G. Camarota, R. Merletti,
and L. Mesin, “Assessment of force and fatigue in isometric
contractions of the upper trapezius muscle by surface EMG
signal and perceived exertion scale,” Gait and Posture, vol. 28,
no. 2, pp. 179–186, 2008.
[26] L. A. C. Kallenberg and H. J. Hermens, “Behaviour of a surface
EMG based measure for motor control: motor unit action
potential rate in relation to force and muscle fatigue,” Journal
of Electromyography and Kinesiology, vol. 18, no. 5, pp. 780–
788, 2008.
[27] Z. Xu and S. Xiao, “Fractal dimension of surface EMG and
its determinants,” in Proceedings of the 1997 19th Annual
International Conference of the IEEE Engineering in Medicine
and Biology Society, pp. 1570–1573, November 1997.
[28] L. Mesin, C. Cescon, M. Gazzoni, R. Merletti, and A. Rainoldi,
“A bi-dimensional index for the selective assessment of
myoelectric manifestations of peripheral and central muscle
fatigue,” Journal of Electromyography and Kinesiology, vol. 19,
no. 5, pp. 851–863, 2009.
[29] S. Kattla and M. M. Lowery, “Fatigue related changes in electromyographic coherence between synergistic hand muscles,”
Experimental Brain Research, vol. 202, no. 1, pp. 89–99, 2010.
[30] S. P. Arjunan and D. K. Kumar, “Decoding subtle forearm
flexions using fractal features of surface electromyogram from
single and multiple sensors,” Journal of NeuroEngineering and
Rehabilitation, vol. 7, no. 1, article no. 53, 2010.
[31] D. Farina, F. Leclerc, L. Arendt-Nielsen, O. Buttelli, and
P. Madeleine, “The change in spatial distribution of upper
trapezius muscle activity is correlated to contraction duration,” Journal of Electromyography and Kinesiology, vol. 18, no.
1, pp. 16–25, 2008.
[32] F. Danion, S. Li, V. M. Zatsiorsky, and M. L. Latash, “Relations
between surface EMG of extrinsic flexors and individual finger
forces support the notion of muscle compartments,” European
Journal of Applied Physiology, vol. 88, no. 1-2, pp. 185–188,
2002.
[33] C. Larivière, D. Gravel, A. B. Arsenault, D. Gagnon, and
P. Loisel, “Muscle recovery from a short fatigue test and
consequence on the reliability of EMG indices of fatigue,”
European Journal of Applied Physiology, vol. 89, no. 2, pp. 171–
176, 2003.
[34] D. G. Allen, G. D. Lamb, and H. Westerblad, “Skeletal muscle
fatigue: cellular mechanisms,” Physiological Reviews, vol. 88,
no. 1, pp. 287–332, 2008.
[35] T. D. Eichelberger and M. Bilodeau, “Central fatigue of the first
dorsal interosseous muscle during low-force and high-force
sustained submaximal contractions,” Clinical Physiology and
Functional Imaging, vol. 27, no. 5, pp. 298–304, 2007.
[36] K. Schmitt, C. DelloRusso, and R. F. Fregosi, “Force-EMG
changes during sustained contractions of a human upper
airway muscle,” Journal of Neurophysiology, vol. 101, no. 2, pp.
558–568, 2009.
International Journal of Photoenergy
[37] J. W. Chung, C. Kim, and W. D. McCall, “Effect of sustained
contraction on motor unit action potentials and EMG power
spectrum of human masticatory muscles,” Journal of Dental
Research, vol. 81, no. 9, pp. 646–649, 2002.
[38] R. Merletti and S. Roy, “Myoelectric and mechanical manifestations of muscle fatigue in voluntary contractions,” Journal
of Orthopaedic and Sports Physical Therapy, vol. 24, no. 6, pp.
342–353, 1996.
[39] K. S. Sunnerhagen, U. Carlsson, A. Sandberg, E. Stålberg,
M. Hedberg, and G. Grimby, “Electrophysiologic evaluation
of muscle fatigue development and recovery in late polio,”
Archives of Physical Medicine and Rehabilitation, vol. 81, no.
6, pp. 770–776, 2000.
[40] R. G. Miller, D. Giannini, H. S. Milner-Brown et al., “Effects of
fatiguing exercise on high-energy phosphates, force, and EMG:
evidence for three phases of recovery,” Muscle and Nerve, vol.
10, no. 9, pp. 810–821, 1987.
[41] L. S. Krivickas, S. F. Nadler, M. R. Davies, G. F. Petroski, and
J. H. Feinberg, “Spectral analysis during fatigue: surface and
fine wire electrode comparison,” American Journal of Physical
Medicine and Rehabilitation, vol. 75, no. 1, pp. 15–20, 1996.
[42] T. L. McIsaac and A. J. Fuglevand, “Motor-unit synchrony
within and across compartments of the human flexor digitorum superficialis,” Journal of Neurophysiology, vol. 97, no. 1,
pp. 550–556, 2007.
[43] D. Falla and D. Farina, “Non-uniform adaptation of motor
unit discharge rates during sustained static contraction of the
upper trapezius muscle,” Experimental Brain Research, vol.
191, no. 3, pp. 363–370, 2008.
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